In this paper, we present a comparative analysis of the forecasting accuracy of univariate and multivariate linear models that incorporate fundamental accounting variables (i.e., inventory, accounts receivable, and so on) with the forecast accuracy of neural network models. Unique to this study is the focus of our comparison on the multivariate models to examine whether the neural network model...

The most common starting point for investors when buying a stock is to look at the trend of price changes. In recent years, different models have been used to predict stock prices by researchers, and since artificial intelligence techniques, including neural networks, genetic algorithms and fuzzy logic, have achieved successful re-sults in solving complex problems; in this regard, more exploita...

This article reports the results of an experimental analysis of the impact of MIS design variables on managers" evaluations of subordinates" performances. The experiment involved examining whether different ways of reporting a specific standard cost accounting situation resulted in differences in managers’ evaluations. When actual costs differed significantly from standard, it was found that th...

In this paper, we compare the forecasting accuracy of two neural network models in forecasting earnings per share of Chinese listed companies based upon fundamental accounting variables. In one neural network model, weights estimated by back propagation were utilised, and in the other model a genetic algorithm was utilised. Based upon a sample of 723 Chinese companies in 22 industries over a te...

This paper examines the capability of both accounting and market information in explaining the cross-sectional variation of five-year credit default swap spreads. The paper proposes a panel FAVAR methodological approach to combine the additional predictions from a long list of accounting and market fundamental variables, while controlling the macroeconomic environment of the firms. A comprehens...